arXiv subjects

Fan Xu

Publications and source records attributed to Fan Xu.

At least 91 records · Page 5Linked to original sources

arXiv2025

An Integral Field Spectroscopy Study Of Thin And Thick Discs In IC 2531

Thin and thick discs are prominent components in the Milky Way and other spiral galaxies, but their formation histories are not yet understood, which is particularly true for thick discs. This is partially due to the fact that we lack sufficient understanding of thin and thick discs in other galaxies to determine how common features of the Milky Way's discs are. Here, we conduct an integral field spectroscopy study of the thin and thick discs in the edge-on Milky Way analogue IC 2531, using observational data from the WiFeS IFU spectrograph on the ANU 2.3m telescope. We provide spectral analysis of IC 2531's kinematics and stellar populations in the thin and thick disc regions by conducting pPXF fitting with Vazdekis models. We found that IC 2531's disc above the dust plane generally has chemical properties between the Milky Way's chemical thin and thick disc. IC 2531's thick disc is somewhat similar to the Milky Way's in terms of stellar populations: on average, it is older, more metal-poor than its corresponding thin disc, but similarly alpha-enhanced in general. But we do find a clearly alpha-rich thick disc bin though one of our "thick disc" bins may be dominated by a warped thin disc. These results may help to constrain formation theories of thick discs.

astro-ph.GA↗

arXiv2025

TBDetector:Transformer-Based Detector for Advanced Persistent Threats with Provenance Graph

APT detection is difficult to detect due to the long-term latency, covert and slow multistage attack patterns of Advanced Persistent Threat (APT). To tackle these issues, we propose TBDetector, a transformer-based advanced persistent threat detection method for APT attack detection. Considering that provenance graphs provide rich historical information and have the powerful attacks historic correlation ability to identify anomalous activities, TBDetector employs provenance analysis for APT detection, which summarizes long-running system execution with space efficiency and utilizes transformer with self-attention based encoder-decoder to extract long-term contextual features of system states to detect slow-acting attacks. Furthermore, we further introduce anomaly scores to investigate the anomaly of different system states, where each state is calculated with an anomaly score corresponding to its similarity score and isolation score. To evaluate the effectiveness of the proposed method, we have conducted experiments on five public datasets, i.e., streamspot, cadets, shellshock, clearscope, and wget_baseline. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.

cs.CR↗

arXiv2025

On the Learning with Augmented Class via Forests

Decision trees and forests have achieved successes in various real applications, most working with all testing classes known in training data. In this work, we focus on learning with augmented class via forests, where an augmented class may appear in testing data yet not in training data. We incorporate information of augmented class into trees' splitting, that is, augmented Gini impurity, a new splitting criterion is introduced to exploit some unlabeled data from testing distribution. We then develop the Learning with Augmented Class via Forests (short for LACForest) approach, which constructs shallow forests according to the augmented Gini impurity and then splits forests with pseudo-labeled augmented instances for better performance. We also develop deep neural forests via an optimization objective based on our augmented Gini impurity, which essentially utilizes the representation power of neural networks for forests. Theoretically, we present the convergence analysis for our augmented Gini impurity, and we finally conduct experiments to evaluate our approaches. The code is available at https://github.com/nju-xuf/LACForest.

cs.LG↗

arXiv2025

Global well-posedness for the Landau-Lifshitz-Baryakhtar equation in $\mathbb{R}^3$

This paper establishes the global well-posedness of the Landau-Lifshitz-Baryakhtar (LLBar) equation in the whole space $\mathbb{R}^3$. The study first demonstrates the existence and uniqueness of global strong solutions using the weak compactness approach. Furthermore, the existence and uniqueness of classical solutions, as well as arbitrary smooth solutions, are derived through a bootstrap argument. The proofs for the existence of these three types of global solutions are based on Friedrichs mollifier approximation and energy estimates, with the structure of the LLBar equation playing a crucial role in the derivation of the results.

math.AP↗

arXiv2025

Existence and uniqueness of global large-data solutions for the Chemotaxis-Navier-Stokes system in $\mathbb{R}^2$

This work investigates the Cauchy problem for the classical Chemotaxis-Navier-Stokes (CNS) system in $\mathbb{R}^2$. We establish the global existence and uniqueness of strong, classical, and arbitrarily smooth solutions under large initial data, which has not been addressed in the existing literature. The key idea is to first derive an entropy-energy estimate for initial data with low regularity, by leveraging the intrinsic entropy structure of the system. Building on this foundation, we then obtain higher-order energy estimates for smoother initial data via a bootstrap argument, in which the parabolic nature of the CNS system plays a crucial role in the iterative control of regularity.

math.AP↗

arXiv2025

OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning

Understanding and modeling enzyme-substrate interactions is crucial for catalytic mechanism research, enzyme engineering, and metabolic engineering. Although a large number of predictive methods have emerged, they do not incorporate prior knowledge of enzyme catalysis to rationally modulate general protein-molecule features that are misaligned with catalytic patterns. To address this issue, we introduce a two-stage progressive framework, OmniESI, for enzyme-substrate interaction prediction through conditional deep learning. By decomposing the modeling of enzyme-substrate interactions into a two-stage progressive process, OmniESI incorporates two conditional networks that respectively emphasize enzymatic reaction specificity and crucial catalysis-related interactions, facilitating a gradual feature modulation in the latent space from general protein-molecule domain to catalysis-aware domain. On top of this unified architecture, OmniESI can adapt to a variety of downstream tasks, including enzyme kinetic parameter prediction, enzyme-substrate pairing prediction, enzyme mutational effect prediction, and enzymatic active site annotation. Under the multi-perspective performance evaluation of in-distribution and out-of-distribution settings, OmniESI consistently delivered superior performance than state-of-the-art specialized methods across seven benchmarks. More importantly, the proposed conditional networks were shown to internalize the fundamental patterns of catalytic efficiency while significantly improving prediction performance, with only negligible parameter increases (0.16%), as demonstrated by ablation studies on key components. Overall, OmniESI represents a unified predictive approach for enzyme-substrate interactions, providing an effective tool for catalytic mechanism cracking and enzyme engineering with strong generalization and broad applicability.

q-bio.BM↗

arXiv2025

Global strong solution for the stochastic tamed Chemotaxis-Navier-Stokes system in $\mathbb{R}^3$

In this work, we consider the 3D Cauchy problem for a coupled system arising in biomathematics, consisting of a chemotaxis model with a cubic logistic source and the stochastic tamed Navier-Stokes equations (STCNS, for short). Our main goal is to establish the existence and uniqueness of a global strong solution (strong in both the probabilistic and PDE senses) for the 3D STCNS system with large initial data. To achieve this, we first introduce a triple approximation scheme by using the Friedrichs mollifier, frequency truncation operators, and cut-off functions. This scheme enables the construction of sufficiently smooth approximate solutions and facilitates the effective application of the entropy-energy method. Then, based on a newly derived stochastic version of the entropy-energy inequality, we further establish some a priori higher-order energy estimates, which together with the stochastic compactness method, allow us to construct the strong solution for the STCNS system.

math.AP↗

arXiv2025

Evidence of competing ground states between fractional Chern insulator and antiferromagnetism in moiré MoTe2

Two-dimensional moire materials present unprecedented opportunities to explore quantum phases of matter arising from the interplay of band topology and strong correlations.One of the most striking examples is the recent observation of fractional quantum anomalous Hall (FQAH) effect in twisted bilayer MoTe$_2$ (tMoTe2) with relatively large twist angles(~3.7deg-3.9deg). The electronic ground states are usually expected to be sensitive to the twist angle, as the twist angle determines the electron bandwidth and correlation strength in the moire system. Here, we report the observation of unexpected competing magnetic ground states in tMoTe2 moire superlattice, on which balance can be tipped by both twist angle and electric field (E). Specifically, we observed anomalous antiferromagnetic (AFM) ground states with zero Hall resistance at both v_h=1 and 2/3, at intermediate twist angles ~3deg. The AFM orders are suppressed by applying vertical E, and emergent ferromagnetism accompanied by integer Chern insulator (ICI) or fractional Chern insulator (FCI) states are observed near the critical E (E_c) of moire superlattice symmetry transition. Our results demonstrate tMoTe2 as a fascinating platform for exploring unexpected correlated phases with nontrivial topology and fractional excitations and point to electric-field-controlled ultralow-power spin-valleytronic devices.

cond-mat.mes-hall↗

arXiv2025

Softness Ratio of SWIFT Gamma-ray Bursts and Relevant Correlations

The properties of X-ray flashes (XRFs) and X-ray rich gamma-ray bursts (XRRs) as compared with classical gamma-ray bursts (C-GRBs) have been widely discussed during the \emph{Swift} era. It has been proposed that XRFs and XRRs are low-energy extensions of the GRB population so that they should follow similar correlations. To further examine this idea, we collect a sample of $303$ GRBs detected by \emph{Swift} over the past two decades, all of which have reliable redshifts and spectral parameters. The bursts are classified into XRFs, XRRs, and C-GRBs based on their softness ratio (SR), which is calculated by dividing the $25-50$ keV fluence with the $50-100$ keV fluence. A strong correlation is found among the isotropic energy $E_{\mathrm{iso}}$, peak luminosity $L_{\mathrm{p}}$, and rest frame burst duration $T_{90, \mathrm{rest}}$, i.e., $E_{\mathrm{iso}} \propto L_{\mathrm{p}}^{0.88\pm0.02} T_{90, \mathrm{rest}}^{0.58\pm0.02}$. Additionally, two tight three-parameter correlations involving SR and the rest-frame peak energy $E_{\mathrm{p}}$ are also derived, i.e. $E_{\mathrm{p}} \propto E_{\mathrm{iso}}^{0.20\pm0.02} \mathrm{SR}^{-2.27\pm0.15}$ and $E_{\mathrm{p}} \propto L_{\mathrm{p}}^{0.17\pm0.02} \mathrm{SR}^{-2.33\pm0.14}$. It is interesting to note that XRFs, XRRs, and C-GRBs all follow the above correlations. The implications of these correlations and their potential application in cosmology are discussed.

astro-ph.HE↗

arXiv2025

Electroactive differential growth and delayed instability in accelerated healing tissues

Guided by experiments contrasting electrically accelerated recovery with natural healing, this study formulates a model to investigate the importance of electroactive differential growth and morphological changes in tissue repair. It underscores the clinical potential of leveraging electroactive differential growth for improved healing outcomes. The study reveals that voltage stimulation significantly enhances the healing and growth of biological tissues, accelerating the regeneration process across various growth modalities and steering towards isotropic growth conditions that do not favor any specific growth pathways. Enhancing the electroelastic coupling parameters improves the efficacy of bioelectric devices, initiating contraction and fortification of biological tissues in alignment with the electric field. This process facilitates swift cell migration and proliferation, as well as oriented growth of tissue. In instances of strain stiffening at elevated strains, the extreme critical growth ratio aligns with the predictions of neo-Hookean models. Conversely, for tissues experiencing strain stiffening under moderate to very low strain conditions, the strain stiffening effect substantially delays the onset of electroelastic growth instability, ultimately producing a smooth, hyperelastic surface devoid of any unstable morphologies. Our investigation, grounded in nonlinear electroelastic field and perturbation theories, explores how electric fields influence differential growth and instability in biological tissues. We examine the interactions among dimensionless voltage, internal pressure, electroelastic coupling, radius ratio, and strain stiffening, revealing their effects on promoting growth and delaying instability. This framework offers insights into the mechanisms behind electroactive growth and its instabilities, contributing valuable knowledge to the tissue healing.

cond-mat.soft↗

arXiv2025

GRB Timing: Decoding the Hidden Slow Jets in GRB 060729

Gamma-ray bursts (GRBs) are luminous stellar explosions characterized by the ejection of relativistic jets. This work proposes a novel paradigm to study these GRB jets. By analyzing the timing information of prompt pulses and X-ray flares, in conjunction with the multi-wavelength afterglow observations, we identify three distinct jets in the extraordinary GRB 060729, with initial bulk Lorentz factors ranging from approximately 20 to 80, smaller than typical values of $> 100$. These three jets undergo two successive collisions, producing the observed pair of X-ray flares. Following these interactions, the system evolves into a fast, narrow jet and a slower, hollow jet that continues to propagate in the circumburst medium, evidenced by the notable twin bumps observed in the X-ray and optical afterglow of GRB 060729. Our findings demonstrate that the timing of the early emission enables us to measure the velocities of the GRB jets. The proposed paradigm enhances our understanding of jet dynamics and shock interactions and serves as a powerful tool for probing the physics of the central engine with the expanded sample in the current golden era of GRB research.

astro-ph.HE↗

arXiv2025

The Soft X-ray Aspect of Gamma-ray Bursts in the Einstein Probe Era

The Einstein Probe (EP) satellite, dedicated at time-domain high-energy astrophysics and multi-messenger astronomy, was recently launched and successfully put into operation. The wide-field X-ray telescope (WXT, 0.5-4 keV) onboard has identified multiple gamma-ray burst (GRB) events, with an average duration of several hundred seconds. This duration is several times longer than the average duration of long gamma-ray bursts (LGRBs) detected by the Neil Gehrels Swift Observatory, which typically stands at several tens of seconds. Additionally, EP has detected some unknown X-ray transients whose connection to GRBs is uncertain, due to the absence of gamma-ray counterparts and efficient follow-up observation at multi-wavelengths. Several main factors could account for the longer time, including the Doppler effect of off-axis viewing, the spectral lag effect of the synchrotron spectrum of cooling electrons, and some unknown prolonged intrinsic X-ray activities. Our studies indicate that EP GRBs may primarily consist of off-axis viewed bursts, forming a unique population among the GRB zoo, yet the intrinsic origin for the specific bursts could not be excluded. By analyzing the statistical properties of the historical LGRB samples, we explored observable properties of on-axis and off-axis LGRBs in the soft X-ray band. The predicted characteristics of off-axis viewed GRBs, including the duration, energy fluence, low-energy spectral index, and the slopes of Amati and Yonetoku relations, could be tested with a larger sample of GRB events detected by EP in the future.

astro-ph.HE↗

arXiv2025

C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation

Despite the rapid advancement of large language models, they remain highly susceptible to generating hallucinations, which significantly hinders their widespread application. Hallucination research requires dynamic and fine-grained evaluation. However, most existing hallucination benchmarks (especially in Chinese language) rely on human annotations, making automatical and cost-effective hallucination evaluation challenging. To address this, we introduce HaluAgent, an agentic framework that automatically constructs fine-grained QA dataset based on some knowledge documents. Our experiments demonstrate that the manually designed rules and prompt optimization can improve the quality of generated data. Using HaluAgent, we construct C-FAITH, a Chinese QA hallucination benchmark created from 1,399 knowledge documents obtained from web scraping, totaling 60,702 entries. We comprehensively evaluate 16 mainstream LLMs with our proposed C-FAITH, providing detailed experimental results and analysis.

cs.CL↗

arXiv2025

Explicit cluster multiplication formulas for the quantum cluster algebra of type $A_2^{(1)}$

Let $Q$ be an affine quiver of type $A_2^{(1)}$. We explicitly construct the cluster multiplication formulas for the quantum cluster algebra of $Q$ with principal coefficients. As applications, we obtain: (1)\ an exact expression for every quantum cluster variable as a polynomial in terms of the quantum cluster variables in clusters which are one-step mutations from the initial cluster; (2)\ an explicit bar-invariant positive $\mathbb{ZP}$-basis.

math.QA↗

arXiv2025

BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting

In practice, physical spatiotemporal forecasting can suffer from data scarcity, because collecting large-scale data is non-trivial, especially for extreme events. Hence, we propose \method{}, a novel probabilistic framework to realize iterative self-training with new self-ensemble strategies, achieving better physical consistency and generalization on extreme events. Following any base forecasting model, we can encode its deterministic outputs into a latent space and retrieve multiple codebook entries to generate probabilistic outputs. Then BeamVQ extends the beam search from discrete spaces to the continuous state spaces in this field. We can further employ domain-specific metrics (e.g., Critical Success Index for extreme events) to filter out the top-k candidates and develop the new self-ensemble strategy by combining the high-quality candidates. The self-ensemble can not only improve the inference quality and robustness but also iteratively augment the training datasets during continuous self-training. Consequently, BeamVQ realizes the exploration of rare but critical phenomena beyond the original dataset. Comprehensive experiments on different benchmarks and backbones show that BeamVQ consistently reduces forecasting MSE (up to 39%), enhancing extreme events detection and proving its effectiveness in handling data scarcity.

cs.LG↗

arXiv2025

RL-GSBridge: 3D Gaussian Splatting Based Real2Sim2Real Method for Robotic Manipulation Learning

Sim-to-Real refers to the process of transferring policies learned in simulation to the real world, which is crucial for achieving practical robotics applications. However, recent Sim2real methods either rely on a large amount of augmented data or large learning models, which is inefficient for specific tasks. In recent years, with the emergence of radiance field reconstruction methods, especially 3D Gaussian splatting, it has become possible to construct realistic real-world scenes. To this end, we propose RL-GSBridge, a novel real-to-sim-to-real framework which incorporates 3D Gaussian Splatting into the conventional RL simulation pipeline, enabling zero-shot sim-to-real transfer for vision-based deep reinforcement learning. We introduce a mesh-based 3D GS method with soft binding constraints, enhancing the rendering quality of mesh models. Then utilizing a GS editing approach to synchronize the rendering with the physics simulator, RL-GSBridge could reflect the visual interactions of the physical robot accurately. Through a series of sim-to-real experiments, including grasping and pick-and-place tasks, we demonstrate that RL-GSBridge maintains a satisfactory success rate in real-world task completion during sim-to-real transfer. Furthermore, a series of rendering metrics and visualization results indicate that our proposed mesh-based 3D GS reduces artifacts in unstructured objects, demonstrating more realistic rendering performance.

cs.RO↗

arXiv2025

Generalized quantum two level model and its application in astrophysics

Complicated time-dependent curved spacetime and electric field are involved in many astrophysical situations, including the early universe, Hawking radiation, the Schwinger effect, and gravitational pair production. In this Letter, a generalized quantum two-level model (GQTLM) is developed, which is applicable to arbitrary time-dependent curved spacetime and electric field. The model is found to be consistent with quantum kinetic theory, and is characterized by its simplicity and versatility. The momentum distribution of particles and the effects of gravitational distortions can be correctly described. Quantum properties concerning vortex structures, such as the intrinsic orbital angular momentum of particles and antiparticles can also be conveniently calculated. The model is expected to significantly advance the quantum exploration of the universe. It could refine the prediction of primordial gravitational waves and relevant non-Gaussian signals, extend the calculation of Hawking radiation to general black hole configurations, help to distinguish neutron stars from strange quark stars, and elucidate the gravitational pair production mechanism.

gr-qc↗

arXiv2025

A Search for Low-frequency Radio Pulses from Long Gamma-ray Bursts with the Murchison Widefield Array

It has been proposed that coherent radio emission could be emitted during or shortly following a gamma-ray burst (GRB). Here we present a low-frequency ($170-200$ MHz) search for radio pulses associated with long-duration GRBs using the Murchison Widefield Array (MWA). The MWA, with its rapid-response system, is capable of performing GRB follow-up observations within approximately $30$ seconds. Our single pulse search, with temporal and spectral resolutions of $100\ μ$s and $10$ kHz, covers dispersion measures up to $5000$ pc cm$^{-3}$. Two single pulse candidates are identified with significance greater than $6σ$, surviving a friends-of-friends analysis. We rule out random fluctuations as their origin at a confidence level of $97\%$ ($2.2σ$). We caution that radio frequency interference from digital TV (DTV) is most likely the origin of these pulses since the DTV frequency bands almost cover the entire observing frequency band. If they are astrophysical signals, we estimate the peak flux densities for our pulse candidates of $3.6\pm0.6$ Jy and $10.5\pm1.5$ Jy, with corresponding fluences of $431\pm74$ Jy ms and $211\pm37$ Jy ms, respectively. Based on these observations and the assumption of a magnetar origin for the pulse, we constrain the radio emission efficiency as $ε_{\rm{r}}\sim10^{-3}$ for both candidates, which is consistent with pulsar observations. Our results highlight the promising potential of new-generation radio telescopes like the MWA to probe the central engines of GRBs.

astro-ph.HE↗